Google is said to be in panic mode over ChatGPT. The company is reportedly preparing to show off at least 20 AI-powered products and a chatbot for its search engine this year, with at least some set to debut at its I/O conference in May. Google reportedly sees ChatGPT as a threat to its business and has sped up development of AI-powered products. Google said AI would be its focus while announcing the layoffs of 12,000 employees this morning, and a report by The New York Times highlights plans to use the tech for search, image generation, and other new products.

Is the fears that #ChatGPT will leave #programmers out of work true? ChatGPT is a helpful tool for programmers skilled enough to verify and modify generated code, but at the same time, ChatGPT lack even rudimentary intelligence. ChatGPT operates exclusively on texts: it looks for something associated with the request, including code and explanations. It can "seamlessly" stitch fragments of texts but cannot use logical reasoning or detect contradictions and absurdities in the generated output. The facts found in the experiments lead to this conclusion: Formally correct #code can be irrational.

Deep learning (DL) has arrived, not only for natural language, speech, and image processing but also for coding, which I refer to as deep programming (DP). DP is used to detect similar programs, find relevant code, translate programs from one language to another, discover software defects, and to synthesize programs from a natural language description. The advent of large transformer language models10 is now being applied to programs with encouraging results. Just like DL is enabled by the enormous amount of textual and image data available on the Internet, DP is enabled by the vast amount of code available in open source repositories such as GitHub, as well as the ability to reuse libraries via modern package managers such as npm and pip. The former is used in the Github Copilot project14 and integrates with development environments to automatically suggest code to developers.

Whether computers can actually "think" and "feel" is a question that has long fascinated society. Alan M. Turing introduced a test for gauging machine intelligence as early as 1950. Movies such as 2001: A Space Odyssey and Star Wars have only served to fuel these thoughts, but while the concept was once confined to science fiction, it is rapidly emerging as a serious topic of discussion. In a few cases, the dialog has become so convincing that people have deemed machines sentient. A recent example involves former Google data scientist Blake Lemoine, who published human-to-machine discussions with an AI system called LaMDA.a

Check out all the on-demand sessions from the Intelligent Security Summit here. Massachusetts-headquartered Akkio offers a no-code platform it says can help enterprises build and deploy artificial intelligence (AI) in minutes. The company has now enhanced its product with a new capability: chat data prep. The feature enables users to prepare and transform large volumes of data by simply typing in what they want in plain conversational language. Data preparation and transformation is one of the first steps in the AI development process.

In today's competitive job market, candidates need to make a strong first impression to stand out from the pack. And for an increasing number of job seekers, that means using an artificially intelligent chatbot to write and edit their job applications. ChatGPT, which was made free and publicly available by the artificial intelligence lab OpenAI in November, is a chatbot powered by machine learning. People can ask it questions and it provides a quick response based on "a massive dataset of human-generated text," as ChatGPT will tell you. ChatGPT's capabilities are remarkable and are already transforming how students write essays and how teachers grade and plan lessons.

I've always been interested in computers because of their ability to help people better understand the world around them. Over the last decade, much of the research done at Google has been in pursuit of a similar vision -- to help people better understand the world around them and get things done. We want to build more capable machines that partner with people to accomplish a huge variety of tasks. Analysis and synthesis tasks, like crafting new documents or emails from a few sentences of guidance, or partnering with people to jointly write software together. We want to solve complex mathematical or scientific problems. Transform modalities, or translate the world's information into any language. Diagnose complex diseases, or understand the physical world. We've demonstrated early versions of some of these capabilities in research artifacts, and we've partnered with many teams across Google to ship some of these capabilities in Google products that touch the lives of billions of users. But the most exciting aspects of this journey still lie ahead! With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond. I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models.

Groundbreaking inventions and highly significant performance improvements in deep learning based Natural Language Processing are witnessed through the development of transformer based large Pre-trained Language Models (PLMs). The wide availability of unlabeled data within human generated data deluge along with self-supervised learning strategy helps to accelerate the success of large PLMs in language generation, language understanding, etc. But at the same time, latent historical bias/unfairness in human minds towards a particular gender, race, etc., encoded unintentionally/intentionally into the corpora harms and questions the utility and efficacy of large PLMs in many real-world applications, particularly for the protected groups. In this paper, we present an extensive investigation towards understanding the existence of "Affective Bias" in large PLMs to unveil any biased association of emotions such as anger, fear, joy, etc., towards a particular gender, race or religion with respect to the downstream task of textual emotion detection. We conduct our exploration of affective bias from the very initial stage of corpus level affective bias analysis by searching for imbalanced distribution of affective words within a domain, in large scale corpora that are used to pre-train and fine-tune PLMs. Later, to quantify affective bias in model predictions, we perform an extensive set of class-based and intensity-based evaluations using various bias evaluation corpora. Our results show the existence of statistically significant affective bias in the PLM based emotion detection systems, indicating biased association of certain emotions towards a particular gender, race, and religion.

Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt. Finding and refining prompts that produce a desired image has become the art of prompt engineering. Generative models do not provide a built-in retrieval model for a user's information need expressed through prompts. In light of an extensive literature review, we reframe prompt engineering for generative models as interactive text-based retrieval on a novel kind of "infinite index". We apply these insights for the first time in a case study on image generation for game design with an expert. Finally, we envision how active learning may help to guide the retrieval of generated images.

If you haven't come across #ChatGPT yet, then what rock have you been hiding under? Every social media platform, news outlet, magazine, blog, podcast and vlog is talking about it. The opportunities provided by #ChatGPT are huge. I talked about it here in this recent post: Will AI Make Us Lazy and Less Creative? I really enjoyed Sara Dietschy's recent vlog on the topic: As Geoff Barton explains in his recent TES article (thanks again Jill Berry for spotlighting that!) the tool is far from perfect.